Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations220.031
Missing cells364.423
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.2 MiB
Average record size in memory144.0 B

Variable types

Numeric9
Text3
Categorical3
DateTime1
Unsupported2

Alerts

city is highly overall correlated with latitude and 2 other fieldsHigh correlation
host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with city and 1 other fieldsHigh correlation
longitude is highly overall correlated with city and 1 other fieldsHigh correlation
neighbourhood_group is highly overall correlated with city and 3 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
price is highly overall correlated with neighbourhood_group and 1 other fieldsHigh correlation
price(€) is highly overall correlated with priceHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
last_review has 54371 (24.7%) missing valuesMissing
reviews_per_month has 54371 (24.7%) missing valuesMissing
neighbourhood_group has 151518 (68.9%) missing valuesMissing
city has 103390 (47.0%) missing valuesMissing
price is highly skewed (γ1 = 85.87746722)Skewed
minimum_nights is highly skewed (γ1 = 26.59625713)Skewed
price(€) is highly skewed (γ1 = 21.19928567)Skewed
id has unique valuesUnique
calculated_host_listings_count is an unsupported type, check if it needs cleaning or further analysisUnsupported
availability_365 is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 54248 (24.7%) zerosZeros

Reproduction

Analysis started2024-10-16 07:56:36.732979
Analysis finished2024-10-16 07:57:01.546078
Duration24.81 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct220031
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22408313
Minimum2539
Maximum50955051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:01.655053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile2627945.5
Q113383695
median22497889
Q331554452
95-th percentile39695005
Maximum50955051
Range50952512
Interquartile range (IQR)18170757

Descriptive statistics

Standard deviation11754902
Coefficient of variation (CV)0.52457773
Kurtosis-0.81637171
Mean22408313
Median Absolute Deviation (MAD)9090535
Skewness-0.045580406
Sum4.9305235 × 1012
Variance1.3817772 × 1014
MonotonicityNot monotonic
2024-10-16T09:57:01.829681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36487245 1
 
< 0.1%
6400 1
 
< 0.1%
23986 1
 
< 0.1%
28300 1
 
< 0.1%
32119 1
 
< 0.1%
32649 1
 
< 0.1%
37256 1
 
< 0.1%
40470 1
 
< 0.1%
42732 1
 
< 0.1%
46536 1
 
< 0.1%
Other values (220021) 220021
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3647 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
ValueCountFrequency (%)
50955051 1
< 0.1%
50950278 1
< 0.1%
50934102 1
< 0.1%
50932398 1
< 0.1%
50932336 1
< 0.1%
50931203 1
< 0.1%
50929172 1
< 0.1%
50928814 1
< 0.1%
50928474 1
< 0.1%
50927557 1
< 0.1%

name
Text

Distinct213293
Distinct (%)97.0%
Missing67
Missing (%)< 0.1%
Memory size1.7 MiB
2024-10-16T09:57:02.324679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length259
Median length165
Mean length37.531196
Min length1

Characters and Unicode

Total characters8.255.512
Distinct characters2.303
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209.517 ?
Unique (%)95.3%

Sample

1st rowThe Studio Milan
2nd row" Characteristic Milanese flat"
3rd rownice flat near the park
4th rowNico & Cynthia's Easy Yellow Suite
5th rowNico&Cinzia's Red Easy Suite!
ValueCountFrequency (%)
in 64425
 
4.8%
room 40633
 
3.0%
37234
 
2.8%
apartment 31574
 
2.4%
bedroom 25575
 
1.9%
flat 20533
 
1.5%
to 19286
 
1.4%
with 17410
 
1.3%
2 17172
 
1.3%
private 16697
 
1.2%
Other values (53153) 1052387
78.4%
2024-10-16T09:57:03.045697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1130782
 
13.7%
e 605345
 
7.3%
o 584481
 
7.1%
a 504894
 
6.1%
t 469537
 
5.7%
n 465533
 
5.6%
i 428172
 
5.2%
r 420095
 
5.1%
l 260230
 
3.2%
s 215491
 
2.6%
Other values (2293) 3170952
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8255512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1130782
 
13.7%
e 605345
 
7.3%
o 584481
 
7.1%
a 504894
 
6.1%
t 469537
 
5.7%
n 465533
 
5.6%
i 428172
 
5.2%
r 420095
 
5.1%
l 260230
 
3.2%
s 215491
 
2.6%
Other values (2293) 3170952
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8255512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1130782
 
13.7%
e 605345
 
7.3%
o 584481
 
7.1%
a 504894
 
6.1%
t 469537
 
5.7%
n 465533
 
5.6%
i 428172
 
5.2%
r 420095
 
5.1%
l 260230
 
3.2%
s 215491
 
2.6%
Other values (2293) 3170952
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8255512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1130782
 
13.7%
e 605345
 
7.3%
o 584481
 
7.1%
a 504894
 
6.1%
t 469537
 
5.7%
n 465533
 
5.6%
i 428172
 
5.2%
r 420095
 
5.1%
l 260230
 
3.2%
s 215491
 
2.6%
Other values (2293) 3170952
38.4%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct144510
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84945278
Minimum1944
Maximum4.1172076 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:03.411712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1944
5-th percentile1788451.5
Q114396024
median46403919
Q31.4150968 × 108
95-th percentile2.6320938 × 108
Maximum4.1172076 × 108
Range4.1171882 × 108
Interquartile range (IQR)1.2711365 × 108

Descriptive statistics

Standard deviation88566074
Coefficient of variation (CV)1.042625
Kurtosis0.25493533
Mean84945278
Median Absolute Deviation (MAD)40903928
Skewness1.1026115
Sum1.8690594 × 1013
Variance7.8439494 × 1015
MonotonicityNot monotonic
2024-10-16T09:57:03.573706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33889201 999
 
0.5%
219517861 327
 
0.1%
27693585 314
 
0.1%
137094377 236
 
0.1%
28820321 233
 
0.1%
156158778 232
 
0.1%
107434423 232
 
0.1%
48165024 213
 
0.1%
36410227 197
 
0.1%
1432477 183
 
0.1%
Other values (144500) 216865
98.6%
ValueCountFrequency (%)
1944 1
 
< 0.1%
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2697 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
ValueCountFrequency (%)
411720762 1
< 0.1%
411489946 1
< 0.1%
411016508 1
< 0.1%
410486696 1
< 0.1%
410265727 1
< 0.1%
410205054 1
< 0.1%
410041372 1
< 0.1%
409925612 1
< 0.1%
409413422 1
< 0.1%
409051230 1
< 0.1%
Distinct31499
Distinct (%)14.4%
Missing706
Missing (%)0.3%
Memory size1.7 MiB
2024-10-16T09:57:04.013857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length33
Mean length6.5376724
Min length1

Characters and Unicode

Total characters1.433.875
Distinct characters898
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17.662 ?
Unique (%)8.1%

Sample

1st rowFrancesca
2nd rowJeremy
3rd rowMarta
4th rowNico&Cinzia
5th rowNico&Cinzia
ValueCountFrequency (%)
5552
 
2.2%
and 2571
 
1.0%
david 1559
 
0.6%
maria 1472
 
0.6%
veeve 1235
 
0.5%
anna 1202
 
0.5%
laura 1183
 
0.5%
michael 1182
 
0.5%
alex 1152
 
0.5%
sarah 1087
 
0.4%
Other values (25792) 237634
92.9%
2024-10-16T09:57:04.578878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 182992
 
12.8%
e 130654
 
9.1%
i 117780
 
8.2%
n 105165
 
7.3%
r 82510
 
5.8%
o 73642
 
5.1%
l 72231
 
5.0%
t 47514
 
3.3%
s 47237
 
3.3%
36755
 
2.6%
Other values (888) 537395
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1433875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 182992
 
12.8%
e 130654
 
9.1%
i 117780
 
8.2%
n 105165
 
7.3%
r 82510
 
5.8%
o 73642
 
5.1%
l 72231
 
5.0%
t 47514
 
3.3%
s 47237
 
3.3%
36755
 
2.6%
Other values (888) 537395
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1433875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 182992
 
12.8%
e 130654
 
9.1%
i 117780
 
8.2%
n 105165
 
7.3%
r 82510
 
5.8%
o 73642
 
5.1%
l 72231
 
5.0%
t 47514
 
3.3%
s 47237
 
3.3%
36755
 
2.6%
Other values (888) 537395
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1433875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 182992
 
12.8%
e 130654
 
9.1%
i 117780
 
8.2%
n 105165
 
7.3%
r 82510
 
5.8%
o 73642
 
5.1%
l 72231
 
5.0%
t 47514
 
3.3%
s 47237
 
3.3%
36755
 
2.6%
Other values (888) 537395
37.5%
Distinct562
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:04.980879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length10.287673
Min length3

Characters and Unicode

Total characters2.263.607
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowTIBALDI
2nd rowNAVIGLI
3rd rowSARPI
4th rowVIALE MONZA
5th rowVIALE MONZA
ValueCountFrequency (%)
ku 11185
 
3.5%
sydney 10611
 
3.3%
and 10600
 
3.3%
westminster 9588
 
3.0%
tower 8246
 
2.6%
hamlets 8246
 
2.6%
chelsea 7131
 
2.2%
east 6592
 
2.1%
hackney 6276
 
2.0%
kensington 6193
 
1.9%
Other values (648) 234273
73.5%
2024-10-16T09:57:05.528877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 196710
 
8.7%
a 179738
 
7.9%
n 144620
 
6.4%
t 124698
 
5.5%
s 117550
 
5.2%
i 116979
 
5.2%
r 113911
 
5.0%
98910
 
4.4%
o 93639
 
4.1%
l 92648
 
4.1%
Other values (56) 984204
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2263607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 196710
 
8.7%
a 179738
 
7.9%
n 144620
 
6.4%
t 124698
 
5.5%
s 117550
 
5.2%
i 116979
 
5.2%
r 113911
 
5.0%
98910
 
4.4%
o 93639
 
4.1%
l 92648
 
4.1%
Other values (56) 984204
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2263607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 196710
 
8.7%
a 179738
 
7.9%
n 144620
 
6.4%
t 124698
 
5.5%
s 117550
 
5.2%
i 116979
 
5.2%
r 113911
 
5.0%
98910
 
4.4%
o 93639
 
4.1%
l 92648
 
4.1%
Other values (56) 984204
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2263607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 196710
 
8.7%
a 179738
 
7.9%
n 144620
 
6.4%
t 124698
 
5.5%
s 117550
 
5.2%
i 116979
 
5.2%
r 113911
 
5.0%
98910
 
4.4%
o 93639
 
4.1%
l 92648
 
4.1%
Other values (56) 984204
43.5%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct98672
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.573053
Minimum-34.135212
Maximum51.68169
Zeros0
Zeros (%)0.0%
Negative36662
Negative (%)16.7%
Memory size1.7 MiB
2024-10-16T09:57:05.696877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-34.135212
5-th percentile-33.893956
Q140.41262
median40.79424
Q351.4962
95-th percentile51.55527
Maximum51.68169
Range85.816902
Interquartile range (IQR)11.08358

Descriptive statistics

Standard deviation30.144854
Coefficient of variation (CV)0.92545375
Kurtosis0.9956733
Mean32.573053
Median Absolute Deviation (MAD)10.66852
Skewness-1.6752658
Sum7167081.3
Variance908.71221
MonotonicityNot monotonic
2024-10-16T09:57:05.847855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.42165 34
 
< 0.1%
45.4884 26
 
< 0.1%
51.49455 24
 
< 0.1%
51.51459 24
 
< 0.1%
51.51571 23
 
< 0.1%
51.5138 23
 
< 0.1%
51.51307 22
 
< 0.1%
51.51375 22
 
< 0.1%
51.51343 22
 
< 0.1%
51.52486 22
 
< 0.1%
Other values (98662) 219789
99.9%
ValueCountFrequency (%)
-34.1352122 1
< 0.1%
-34.12623664 1
< 0.1%
-34.09899758 1
< 0.1%
-34.09853882 1
< 0.1%
-34.09440025 1
< 0.1%
-34.09254562 1
< 0.1%
-34.08949817 1
< 0.1%
-34.08949651 1
< 0.1%
-34.08937477 1
< 0.1%
-34.08848048 1
< 0.1%
ValueCountFrequency (%)
51.68169 1
< 0.1%
51.6792 1
< 0.1%
51.67651 1
< 0.1%
51.67566 1
< 0.1%
51.67501 1
< 0.1%
51.67393 1
< 0.1%
51.67344 1
< 0.1%
51.67297 1
< 0.1%
51.67219 1
< 0.1%
51.67215 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct108328
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.428135
Minimum-74.24442
Maximum151.33981
Zeros0
Zeros (%)0.0%
Negative147948
Negative (%)67.2%
Memory size1.7 MiB
2024-10-16T09:57:06.020371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-73.98507
Q1-3.70587
median-0.12838
Q39.199535
95-th percentile151.25387
Maximum151.33981
Range225.58423
Interquartile range (IQR)12.905405

Descriptive statistics

Standard deviation76.030471
Coefficient of variation (CV)4.6280646
Kurtosis-0.54913921
Mean16.428135
Median Absolute Deviation (MAD)9.30053
Skewness0.77143012
Sum3614699
Variance5780.6325
MonotonicityNot monotonic
2024-10-16T09:57:06.177665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.707 37
 
< 0.1%
9.21423 26
 
< 0.1%
-3.70217 20
 
< 0.1%
-3.70409 19
 
< 0.1%
-3.70712 19
 
< 0.1%
-3.70443 19
 
< 0.1%
9.19091 18
 
< 0.1%
-73.95427 18
 
< 0.1%
-3.70502 18
 
< 0.1%
-73.95677 18
 
< 0.1%
Other values (108318) 219819
99.9%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
ValueCountFrequency (%)
151.3398112 1
< 0.1%
151.339805 1
< 0.1%
151.3397888 1
< 0.1%
151.3397674 1
< 0.1%
151.3396904 1
< 0.1%
151.3396779 1
< 0.1%
151.3395821 1
< 0.1%
151.3395529 1
< 0.1%
151.3395483 1
< 0.1%
151.3392876 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Entire home/apt
128154 
Private room
86512 
Shared room
 
4012
Hotel room
 
1353

Length

Max length15
Median length15
Mean length13.716776
Min length10

Characters and Unicode

Total characters3.018.116
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 128154
58.2%
Private room 86512
39.3%
Shared room 4012
 
1.8%
Hotel room 1353
 
0.6%

Length

2024-10-16T09:57:06.325659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T09:57:06.457634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 128154
29.1%
home/apt 128154
29.1%
room 91877
20.9%
private 86512
19.7%
shared 4012
 
0.9%
hotel 1353
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 348185
11.5%
t 344173
11.4%
o 313261
10.4%
r 310555
10.3%
220031
 
7.3%
m 220031
 
7.3%
a 218678
 
7.2%
i 214666
 
7.1%
h 132166
 
4.4%
n 128154
 
4.2%
Other values (9) 568216
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3018116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 348185
11.5%
t 344173
11.4%
o 313261
10.4%
r 310555
10.3%
220031
 
7.3%
m 220031
 
7.3%
a 218678
 
7.2%
i 214666
 
7.1%
h 132166
 
4.4%
n 128154
 
4.2%
Other values (9) 568216
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3018116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 348185
11.5%
t 344173
11.4%
o 313261
10.4%
r 310555
10.3%
220031
 
7.3%
m 220031
 
7.3%
a 218678
 
7.2%
i 214666
 
7.1%
h 132166
 
4.4%
n 128154
 
4.2%
Other values (9) 568216
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3018116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 348185
11.5%
t 344173
11.4%
o 313261
10.4%
r 310555
10.3%
220031
 
7.3%
m 220031
 
7.3%
a 218678
 
7.2%
i 214666
 
7.1%
h 132166
 
4.4%
n 128154
 
4.2%
Other values (9) 568216
18.8%

price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1566
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean917.8157
Minimum0
Maximum1000046
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:06.611659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q155
median99
Q3177
95-th percentile3013
Maximum1000046
Range1000046
Interquartile range (IQR)122

Descriptive statistics

Standard deviation8285.2166
Coefficient of variation (CV)9.0271026
Kurtosis9849.2478
Mean917.8157
Median Absolute Deviation (MAD)51
Skewness85.877467
Sum2.0194791 × 108
Variance68644813
MonotonicityNot monotonic
2024-10-16T09:57:06.781631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7761
 
3.5%
50 7007
 
3.2%
150 6719
 
3.1%
60 5996
 
2.7%
80 5439
 
2.5%
120 5120
 
2.3%
40 5006
 
2.3%
75 4392
 
2.0%
90 4385
 
2.0%
35 4093
 
1.9%
Other values (1556) 164113
74.6%
ValueCountFrequency (%)
0 50
 
< 0.1%
1 1
 
< 0.1%
4 1
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 24
 
< 0.1%
9 35
 
< 0.1%
10 137
0.1%
11 53
 
< 0.1%
12 98
< 0.1%
ValueCountFrequency (%)
1000046 9
< 0.1%
899977 1
 
< 0.1%
749980 1
 
< 0.1%
689509 1
 
< 0.1%
392206 1
 
< 0.1%
299992 1
 
< 0.1%
249958 1
 
< 0.1%
211652 1
 
< 0.1%
200031 3
 
< 0.1%
149996 1
 
< 0.1%

minimum_nights
Real number (ℝ)

SKEWED 

Distinct164
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2580227
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:06.945666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile21
Maximum1250
Range1249
Interquartile range (IQR)3

Descriptive statistics

Standard deviation20.118261
Coefficient of variation (CV)3.8262027
Kurtosis1129.4337
Mean5.2580227
Median Absolute Deviation (MAD)1
Skewness26.596257
Sum1156928
Variance404.74443
MonotonicityNot monotonic
2024-10-16T09:57:07.112630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 72425
32.9%
2 60093
27.3%
3 32232
14.6%
5 11977
 
5.4%
4 11538
 
5.2%
7 10007
 
4.5%
30 6004
 
2.7%
6 2962
 
1.3%
14 2433
 
1.1%
10 1878
 
0.9%
Other values (154) 8482
 
3.9%
ValueCountFrequency (%)
1 72425
32.9%
2 60093
27.3%
3 32232
14.6%
4 11538
 
5.2%
5 11977
 
5.4%
6 2962
 
1.3%
7 10007
 
4.5%
8 402
 
0.2%
9 212
 
0.1%
10 1878
 
0.9%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1125 6
< 0.1%
1124 4
< 0.1%
1118 1
 
< 0.1%
1000 8
< 0.1%
999 7
< 0.1%
900 1
 
< 0.1%
800 2
 
< 0.1%
750 1
 
< 0.1%
720 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct548
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.129164
Minimum0
Maximum896
Zeros54248
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:07.278669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q319
95-th percentile98
Maximum896
Range896
Interquartile range (IQR)18

Descriptive statistics

Standard deviation43.012277
Coefficient of variation (CV)2.1368139
Kurtosis32.419453
Mean20.129164
Median Absolute Deviation (MAD)4
Skewness4.6628449
Sum4429040
Variance1850.056
MonotonicityNot monotonic
2024-10-16T09:57:07.442668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54248
24.7%
1 24326
 
11.1%
2 15146
 
6.9%
3 11127
 
5.1%
4 8707
 
4.0%
5 6964
 
3.2%
6 5958
 
2.7%
7 5171
 
2.4%
8 4594
 
2.1%
9 4063
 
1.8%
Other values (538) 79727
36.2%
ValueCountFrequency (%)
0 54248
24.7%
1 24326
11.1%
2 15146
 
6.9%
3 11127
 
5.1%
4 8707
 
4.0%
5 6964
 
3.2%
6 5958
 
2.7%
7 5171
 
2.4%
8 4594
 
2.1%
9 4063
 
1.8%
ValueCountFrequency (%)
896 1
< 0.1%
825 1
< 0.1%
756 1
< 0.1%
716 1
< 0.1%
706 1
< 0.1%
682 1
< 0.1%
658 1
< 0.1%
654 1
< 0.1%
652 1
< 0.1%
648 1
< 0.1%

last_review
Date

MISSING 

Distinct2804
Distinct (%)1.7%
Missing54371
Missing (%)24.7%
Memory size1.7 MiB
Minimum2010-04-19 00:00:00
Maximum2021-12-06 00:00:00
2024-10-16T09:57:07.601635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:57:07.778668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1125
Distinct (%)0.7%
Missing54371
Missing (%)24.7%
Infinite0
Infinite (%)0.0%
Mean1.2567716
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:07.951667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.2
median0.69
Q31.8
95-th percentile4.3
Maximum58.5
Range58.49
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.5244489
Coefficient of variation (CV)1.2129881
Kurtosis33.752985
Mean1.2567716
Median Absolute Deviation (MAD)0.58
Skewness2.9701195
Sum208196.78
Variance2.3239445
MonotonicityNot monotonic
2024-10-16T09:57:08.115633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3884
 
1.8%
0.04 3476
 
1.6%
0.03 3216
 
1.5%
0.02 2878
 
1.3%
0.09 2808
 
1.3%
0.05 2806
 
1.3%
0.06 2800
 
1.3%
0.08 2496
 
1.1%
0.07 2477
 
1.1%
0.1 2166
 
1.0%
Other values (1115) 136653
62.1%
(Missing) 54371
 
24.7%
ValueCountFrequency (%)
0.01 277
 
0.1%
0.02 2878
1.3%
0.03 3216
1.5%
0.04 3476
1.6%
0.05 2806
1.3%
0.06 2800
1.3%
0.07 2477
1.1%
0.08 2496
1.1%
0.09 2808
1.3%
0.1 2166
1.0%
ValueCountFrequency (%)
58.5 1
< 0.1%
51.21 1
< 0.1%
45.15 1
< 0.1%
39.38 1
< 0.1%
27.95 1
< 0.1%
23.73 1
< 0.1%
23.69 1
< 0.1%
23.28 1
< 0.1%
20.94 1
< 0.1%
20.13 1
< 0.1%

calculated_host_listings_count
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.7 MiB

availability_365
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.7 MiB

price(€)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3180
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.51577
Minimum0
Maximum14843.87
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-10-16T09:57:08.292084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.17
Q152.83
median88.98
Q3149.71
95-th percentile354.71
Maximum14843.87
Range14843.87
Interquartile range (IQR)96.88

Descriptive statistics

Standard deviation274.37403
Coefficient of variation (CV)2.0246649
Kurtosis643.09845
Mean135.51577
Median Absolute Deviation (MAD)43.05
Skewness21.199286
Sum29817671
Variance75281.107
MonotonicityNot monotonic
2024-10-16T09:57:08.460025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.12 3116
 
1.4%
120.24 3066
 
1.4%
48.1 2750
 
1.2%
42.08 2450
 
1.1%
144.29 2393
 
1.1%
180.36 2374
 
1.1%
54.11 2325
 
1.1%
36.07 2307
 
1.0%
72.15 2277
 
1.0%
96.19 2254
 
1.0%
Other values (3170) 194719
88.5%
ValueCountFrequency (%)
0 50
< 0.1%
1.2 1
 
< 0.1%
3.67 2
 
< 0.1%
4.81 1
 
< 0.1%
7.21 1
 
< 0.1%
7.34 2
 
< 0.1%
7.35 2
 
< 0.1%
8 3
 
< 0.1%
8.07 46
< 0.1%
8.42 4
 
< 0.1%
ValueCountFrequency (%)
14843.87 1
 
< 0.1%
12024.2 1
 
< 0.1%
12023 3
 
< 0.1%
11999 1
 
< 0.1%
10000 1
 
< 0.1%
9999 17
< 0.1%
9856 2
 
< 0.1%
9785 1
 
< 0.1%
9619.36 1
 
< 0.1%
9441.4 1
 
< 0.1%

neighbourhood_group
Categorical

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)< 0.1%
Missing151518
Missing (%)68.9%
Memory size1.7 MiB
Manhattan
21661 
Brooklyn
20104 
Centro
8649 
Queens
5666 
Salamanca
 
1324
Other values (21)
11109 

Length

Max length21
Median length18
Mean length8.2834207
Min length5

Characters and Unicode

Total characters567.522
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChamartín
2nd rowLatina
3rd rowArganzuela
4th rowCentro
5th rowArganzuela

Common Values

ValueCountFrequency (%)
Manhattan 21661
 
9.8%
Brooklyn 20104
 
9.1%
Centro 8649
 
3.9%
Queens 5666
 
2.6%
Salamanca 1324
 
0.6%
Chamberí 1252
 
0.6%
Arganzuela 1104
 
0.5%
Bronx 1091
 
0.5%
Tetuán 816
 
0.4%
Carabanchel 708
 
0.3%
Other values (16) 6138
 
2.8%
(Missing) 151518
68.9%

Length

2024-10-16T09:57:08.635991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manhattan 21661
29.1%
brooklyn 20104
27.0%
centro 8649
 
11.6%
queens 5666
 
7.6%
1366
 
1.8%
salamanca 1324
 
1.8%
chamberí 1252
 
1.7%
arganzuela 1104
 
1.5%
bronx 1091
 
1.5%
tetuán 816
 
1.1%
Other values (26) 11476
15.4%

Most occurring characters

ValueCountFrequency (%)
a 89087
15.7%
n 87847
15.5%
t 56484
10.0%
o 52589
9.3%
r 36834
 
6.5%
e 30021
 
5.3%
l 29471
 
5.2%
h 24201
 
4.3%
M 22333
 
3.9%
B 21864
 
3.9%
Other values (32) 116791
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 89087
15.7%
n 87847
15.5%
t 56484
10.0%
o 52589
9.3%
r 36834
 
6.5%
e 30021
 
5.3%
l 29471
 
5.2%
h 24201
 
4.3%
M 22333
 
3.9%
B 21864
 
3.9%
Other values (32) 116791
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 89087
15.7%
n 87847
15.5%
t 56484
10.0%
o 52589
9.3%
r 36834
 
6.5%
e 30021
 
5.3%
l 29471
 
5.2%
h 24201
 
4.3%
M 22333
 
3.9%
B 21864
 
3.9%
Other values (32) 116791
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 89087
15.7%
n 87847
15.5%
t 56484
10.0%
o 52589
9.3%
r 36834
 
6.5%
e 30021
 
5.3%
l 29471
 
5.2%
h 24201
 
4.3%
M 22333
 
3.9%
B 21864
 
3.9%
Other values (32) 116791
20.6%

city
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing103390
Missing (%)47.0%
Memory size1.7 MiB
New York
48895 
Sidney
36662 
Madrid
19618 
Tokyo
11466 

Length

Max length8
Median length6
Mean length6.7400828
Min length5

Characters and Unicode

Total characters786.170
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTokyo
2nd rowTokyo
3rd rowTokyo
4th rowTokyo
5th rowTokyo

Common Values

ValueCountFrequency (%)
New York 48895
22.2%
Sidney 36662
 
16.7%
Madrid 19618
 
8.9%
Tokyo 11466
 
5.2%
(Missing) 103390
47.0%

Length

2024-10-16T09:57:08.780025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T09:57:08.906024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
new 48895
29.5%
york 48895
29.5%
sidney 36662
22.1%
madrid 19618
11.9%
tokyo 11466
 
6.9%

Most occurring characters

ValueCountFrequency (%)
e 85557
10.9%
d 75898
9.7%
o 71827
9.1%
r 68513
 
8.7%
k 60361
 
7.7%
i 56280
 
7.2%
48895
 
6.2%
w 48895
 
6.2%
Y 48895
 
6.2%
N 48895
 
6.2%
Other values (6) 172154
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 786170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 85557
10.9%
d 75898
9.7%
o 71827
9.1%
r 68513
 
8.7%
k 60361
 
7.7%
i 56280
 
7.2%
48895
 
6.2%
w 48895
 
6.2%
Y 48895
 
6.2%
N 48895
 
6.2%
Other values (6) 172154
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 786170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 85557
10.9%
d 75898
9.7%
o 71827
9.1%
r 68513
 
8.7%
k 60361
 
7.7%
i 56280
 
7.2%
48895
 
6.2%
w 48895
 
6.2%
Y 48895
 
6.2%
N 48895
 
6.2%
Other values (6) 172154
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 786170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 85557
10.9%
d 75898
9.7%
o 71827
9.1%
r 68513
 
8.7%
k 60361
 
7.7%
i 56280
 
7.2%
48895
 
6.2%
w 48895
 
6.2%
Y 48895
 
6.2%
N 48895
 
6.2%
Other values (6) 172154
21.9%

Interactions

2024-10-16T09:56:58.572250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:47.103385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:48.775790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:50.310790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:51.903577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.345060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.643667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.964319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.267249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:58.715250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:47.318379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:48.932795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:50.474793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:52.097577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.489022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.787631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.116360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.412282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:58.862251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:47.485384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:49.087793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:50.707577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:52.260605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.628021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.936632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.254352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.569267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:59.002553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:47.632379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:49.253818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:50.881576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:52.408576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.761057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.081318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.388388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.701253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:59.151516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:47.791391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:49.424791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:51.032581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:52.574575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.903065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.226317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.522388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.841252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:59.298514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:48.022379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:49.622794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:51.206610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:52.718578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.050031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.369283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.668356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.982286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:59.465059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:48.259378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:49.782795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:51.387577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:52.876447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.204055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.513317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.818357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:58.141247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:59.640099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:48.420792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:49.931791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:51.547579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.011054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.344052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.653316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:56.950387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:58.274284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:59.787058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:48.594791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:50.125820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:51.724580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:53.168075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:54.488631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:55.802325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:57.104352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T09:56:58.422282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-16T09:57:09.008992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cityhost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewspriceprice(€)reviews_per_monthroom_type
city1.0000.2750.3750.7081.0000.0171.0000.0760.0260.0260.0220.110
host_id0.2751.0000.529-0.0190.040-0.1790.142-0.141-0.003-0.0680.2120.058
id0.3750.5291.0000.082-0.004-0.1270.175-0.318-0.0050.0030.2830.049
latitude0.708-0.0190.0821.000-0.229-0.0501.0000.029-0.2420.114-0.0160.064
longitude1.0000.040-0.004-0.2291.000-0.0611.000-0.0830.119-0.103-0.0180.092
minimum_nights0.017-0.179-0.127-0.050-0.0611.0000.025-0.1330.0890.130-0.2590.005
neighbourhood_group1.0000.1420.1751.0001.0000.0251.0000.0621.0000.0410.0460.145
number_of_reviews0.076-0.141-0.3180.029-0.083-0.1330.0621.000-0.054-0.0770.6960.014
price0.026-0.003-0.005-0.2420.1190.0891.000-0.0541.0000.8290.0580.000
price(€)0.026-0.0680.0030.114-0.1030.1300.041-0.0770.8291.000-0.0220.028
reviews_per_month0.0220.2120.283-0.016-0.018-0.2590.0460.6960.058-0.0221.0000.024
room_type0.1100.0580.0490.0640.0920.0050.1450.0140.0000.0280.0241.000

Missing values

2024-10-16T09:57:00.024088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T09:57:00.538096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-16T09:57:01.207093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365price(€)neighbourhood_groupcity
06400The Studio Milan13822FrancescaTIBALDI45.441199.17813Private room1004122010-04-190.141358100.0NaNNaN
123986" Characteristic Milanese flat"95941JeremyNAVIGLI45.448069.17373Entire home/apt1501152020-07-090.211363150.0NaNNaN
228300nice flat near the park121663MartaSARPI45.476479.17359Private room180182012-04-220.111365180.0NaNNaN
332119Nico & Cynthia's Easy Yellow Suite138683Nico&CinziaVIALE MONZA45.520149.22300Entire home/apt752152018-01-070.23320075.0NaNNaN
432649Nico&Cinzia's Red Easy Suite!138683Nico&CinziaVIALE MONZA45.518749.22495Entire home/apt712292016-10-230.71330871.0NaNNaN
537256COZY FULLY FURNISHED PRIVATE STUDIO CITY CENTER119002GiancarloBUENOS AIRES - VENEZIA45.468849.20777Private room552342019-05-130.492055.0NaNNaN
640470Giacinto Cosy & clean flat near MM1174203GiacintoVIALE MONZA45.520239.22747Entire home/apt753372017-07-240.33235075.0NaNNaN
742732Navigli near down town, linked Expo186608FrancescoMAGENTA - S. VITTORE45.458149.17654Entire home/apt1992142018-04-220.202362199.0NaNNaN
846536Nico & Cinzia's Pink Suite!138683Nico&CinziaVIALE MONZA45.522769.22478Entire home/apt762272018-03-070.23315076.0NaNNaN
955055BEAUTIFUL MODERN ATTIC CENTER OF MI246217CristinaBUENOS AIRES - VENEZIA45.480969.21686Entire home/apt145322016-04-160.031365145.0NaNNaN
idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365price(€)neighbourhood_groupcity
22002136482809Stunning Bedroom NYC! Walking to Central Park!!131529729KendallEast Harlem40.79633-73.93605Private room7520NaTNaN235368.55ManhattanNew York
22002236483010Comfy 1 Bedroom in Midtown East274311461ScottMidtown40.75561-73.96723Entire home/apt20060NaTNaN1176182.80ManhattanNew York
22002336483152Garden Jewel Apartment in Williamsburg New York208514239MelkiWilliamsburg40.71232-73.94220Entire home/apt17010NaTNaN3365155.38BrooklynNew York
22002436484087Spacious Room w/ Private Rooftop, Central location274321313KatHell's Kitchen40.76392-73.99183Private room12540NaTNaN131114.25ManhattanNew York
22002536484363QUIT PRIVATE HOUSE107716952MichaelJamaica40.69137-73.80844Private room6510NaTNaN216359.41QueensNew York
22002636484665Charming one bedroom - newly renovated rowhouse8232441SabrinaBedford-Stuyvesant40.67853-73.94995Private room7020NaTNaN2963.98BrooklynNew York
22002736485057Affordable room in Bushwick/East Williamsburg6570630MarisolBushwick40.70184-73.93317Private room4040NaTNaN23636.56BrooklynNew York
22002836485431Sunny Studio at Historical Neighborhood23492952Ilgar & AyselHarlem40.81475-73.94867Entire home/apt115100NaTNaN127105.11ManhattanNew York
2200293648560943rd St. Time Square-cozy single bed30985759TazHell's Kitchen40.75751-73.99112Shared room5510NaTNaN6250.27ManhattanNew York
22003036487245Trendy duplex in the very heart of Hell's Kitchen68119814ChristopheHell's Kitchen40.76404-73.98933Private room9070NaTNaN12382.26ManhattanNew York